SCTK Cell QC Report
September 12, 2022
1 Introduction
Comprehensive quality control (QC) of single-cell RNA-seq data was
performed with the singleCellTK
package. This report contains information about each QC tool and
visualization of the QC metrics for each sample. For more information on
running this pipeline and performing quality control, see the documentation.
If you use the singleCellTK package for quality control, please include
a reference
in your publication.
2 Summary Statistics
2.1 SCTK-QC
| d8b737fb | 8628f96c | e7372715 | 84824920 | c47a5959 | All Samples | |
|---|---|---|---|---|---|---|
| Number of Cells | 4114 | 2920 | 5464 | 3683 | 3804 | 19985 |
| Mean counts | 6792.4 | 2291 | 3494 | 3253.4 | 2280 | 3721.8 |
| Median counts | 6925.5 | 1520.5 | 2705.5 | 2272 | 1639 | 2581 |
| Mean features detected | 2533.1 | 1232.8 | 1651.8 | 1554.9 | 1219.8 | 1671.9 |
| Median features detected | 2670.5 | 1005 | 1526 | 1358 | 1038 | 1467 |
| scDblFinder - Number of doublets | 198 | 123 | 413 | 214 | 360 | 1308 |
| scDblFinder - Percentage of doublets | 4.81 | 4.21 | 7.56 | 5.81 | 9.46 | 6.54 |
| DoubletFinder - Number of doublets, Resolution 1.5 | 309 | 219 | 410 | 276 | 285 | 1499 |
| DoubletFinder - Percentage of doublets, Resolution 1.5 | 7.51 | 7.5 | 7.5 | 7.49 | 7.49 | 7.5 |
| CXDS - Number of doublets | 361 | 399 | 744 | 560 | 407 | 2471 |
| CXDS - Percentage of doublets | 8.77 | 13.7 | 13.6 | 15.2 | 10.7 | 12.4 |
| BCDS - Number of doublets | 158 | 252 | 371 | 438 | 240 | 1459 |
| BCDS - Percentage of doublets | 3.84 | 8.63 | 6.79 | 11.9 | 6.31 | 7.3 |
| SCDS Hybrid - Number of doublets | 195 | 374 | 443 | 523 | 380 | 1915 |
| SCDS Hybrid - Percentage of doublets | 4.74 | 12.8 | 8.11 | 14.2 | 9.99 | 9.58 |
| DecontX - Mean contamination | 0.0649 | 0.0626 | 0.104 | 0.115 | 0.125 | 0.0961 |
| DecontX - Median contamination | 0.0335 | 0.0314 | 0.0697 | 0.0677 | 0.08 | 0.0542 |
The summary statistics table summarizes QC metrics of the cell matrix. This table summarizes the mean and median of UMI counts and median of genes detected per cell, as well as the number and percentages of doublets and estimated ambient RNA scores per dataset.
3 General quality control metrics
SingleCellTK utilizes the scater
package to compute cell-level QC metrics. The wrapper function
runPerCellQC can be used to separately compute QC metrics
on its own. The wrapper function plotRunPerCellQCResults
can be used to plot the general QC outputs. The QC outputs are
sum, detected, and percent_top_X.
sum contains the total number of counts for each cell.
detected contains the total number of features for each
cell. percent_top_X contains the percentage of the total
counts that is made up by the expression of the top X genes for each
cell. The subsets_ columns contain information for the
specific gene list that was used. For instance, if a gene list
containing mitochondrial genes named mito was used,
subsets_mito_sum would contains the total number of
mitochondrial counts for each cell.
3.1 Total Counts
3.2 Total Features
3.3 Percentage of Library Size Occupied by Top 50 Expressed Features
3.4 Parameters
| useAssay | counts |
| collectionName | NULL |
| geneSetList | NULL |
| geneSetListLocation | rownames |
| mitoRef | NULL |
| mitoIDType | NULL |
| mitoPrefix | NULL |
| mitoID | NULL |
| mitoGeneLocation | NULL |
| percent_top | 50 100 200 500 |
| use_altexps | FALSE |
| flatten | TRUE |
| detectionLimit | 0 |
| packageVersion | 1.22.1 |
In this function, the inSCE parameter is the input
SingleCellExperiment object, while the useAssay parameter
is the assay object that in the SingleCellExperiment object the user
wishes to use.
4 Doublet Detection
4.1 Doublet Detection Summary
4.1.1 scDblFinder
4.1.2 Scds_Cxds
4.1.3 Scds_Bcds
4.1.4 Scds_Hybrid
4.1.5 doubletFinder_1.5
4.2 DoubletFinder
DoubletFinder is a doublet detection algorithm which depends on the
single cell analysis package Seurat.
The wrapper function runDoubletFinder can be used to
separately run the DoubletFinder algorithm on its own. The wrapper
function plotDoubletFinderResults can be used to plot the
QC outputs from the DoubletFinder algorithm. The DoubletFinder outputs
are doubletFinder_doublet_score, which is a numeric
variable of the likelihood that a cell is a doublet, and the
doubletFinder_doublet_label, which is the assignment of
whether the cell is a doublet.
4.2.1 d8b737fb
4.2.1.1 Resolution: 1.5
4.2.1.1.1 DoubletFinder Doublet Assignment
4.2.1.1.2 DoubletFinder Doublet Score
4.2.1.1.3 Density of Doublet Score
4.2.1.1.4 Violin of Doublet Score
4.2.1.1.5 Parameters
| useAssay | counts |
| seed | 12345 |
| seuratNfeatures | 2000 |
| seuratPcs | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
| seuratRes | 1.5 |
| formationRate | 0.075 |
| nCores | NULL |
| verbose | FALSE |
| packageVersion | 2.0.2 |
runDoubletFinder relies on a parameter (in Seurat)
called resolution to determine cells that may be doublets. Users will be
able to manipulate the resolution parameter through
seuratRes. If multiple numeric vectors are stored in
seuratRes, there will be multiple label/scores. The
seuratNfeatures parameter determines the number of features
that is used in the FindVariableFeatures function in
Seurat. seuratPcs parameter determines the number of
dimensions used in the FindNeighbors function in Seurat.
The formationRate parameter is the estimated doublet
detection rate in the dataset. aims to detect doublets by creating
simulated doublets from combining transcriptomic profiles of existing
cells in the dataset.
4.2.2 8628f96c
4.2.2.1 Resolution: 1.5
4.2.2.1.1 DoubletFinder Doublet Assignment
4.2.2.1.2 DoubletFinder Doublet Score
4.2.2.1.3 Density of Doublet Score
4.2.2.1.4 Violin of Doublet Score
4.2.2.1.5 Parameters
| useAssay | counts |
| seed | 12345 |
| seuratNfeatures | 2000 |
| seuratPcs | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
| seuratRes | 1.5 |
| formationRate | 0.075 |
| nCores | NULL |
| verbose | FALSE |
| packageVersion | 2.0.2 |
runDoubletFinder relies on a parameter (in Seurat)
called resolution to determine cells that may be doublets. Users will be
able to manipulate the resolution parameter through
seuratRes. If multiple numeric vectors are stored in
seuratRes, there will be multiple label/scores. The
seuratNfeatures parameter determines the number of features
that is used in the FindVariableFeatures function in
Seurat. seuratPcs parameter determines the number of
dimensions used in the FindNeighbors function in Seurat.
The formationRate parameter is the estimated doublet
detection rate in the dataset. aims to detect doublets by creating
simulated doublets from combining transcriptomic profiles of existing
cells in the dataset.
4.2.3 e7372715
4.2.3.1 Resolution: 1.5
4.2.3.1.1 DoubletFinder Doublet Assignment
4.2.3.1.2 DoubletFinder Doublet Score
4.2.3.1.3 Density of Doublet Score
4.2.3.1.4 Violin of Doublet Score
4.2.3.1.5 Parameters
| useAssay | counts |
| seed | 12345 |
| seuratNfeatures | 2000 |
| seuratPcs | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
| seuratRes | 1.5 |
| formationRate | 0.075 |
| nCores | NULL |
| verbose | FALSE |
| packageVersion | 2.0.2 |
runDoubletFinder relies on a parameter (in Seurat)
called resolution to determine cells that may be doublets. Users will be
able to manipulate the resolution parameter through
seuratRes. If multiple numeric vectors are stored in
seuratRes, there will be multiple label/scores. The
seuratNfeatures parameter determines the number of features
that is used in the FindVariableFeatures function in
Seurat. seuratPcs parameter determines the number of
dimensions used in the FindNeighbors function in Seurat.
The formationRate parameter is the estimated doublet
detection rate in the dataset. aims to detect doublets by creating
simulated doublets from combining transcriptomic profiles of existing
cells in the dataset.
4.2.4 84824920
4.2.4.1 Resolution: 1.5
4.2.4.1.1 DoubletFinder Doublet Assignment
4.2.4.1.2 DoubletFinder Doublet Score
4.2.4.1.3 Density of Doublet Score
4.2.4.1.4 Violin of Doublet Score
4.2.4.1.5 Parameters
| useAssay | counts |
| seed | 12345 |
| seuratNfeatures | 2000 |
| seuratPcs | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
| seuratRes | 1.5 |
| formationRate | 0.075 |
| nCores | NULL |
| verbose | FALSE |
| packageVersion | 2.0.2 |
runDoubletFinder relies on a parameter (in Seurat)
called resolution to determine cells that may be doublets. Users will be
able to manipulate the resolution parameter through
seuratRes. If multiple numeric vectors are stored in
seuratRes, there will be multiple label/scores. The
seuratNfeatures parameter determines the number of features
that is used in the FindVariableFeatures function in
Seurat. seuratPcs parameter determines the number of
dimensions used in the FindNeighbors function in Seurat.
The formationRate parameter is the estimated doublet
detection rate in the dataset. aims to detect doublets by creating
simulated doublets from combining transcriptomic profiles of existing
cells in the dataset.
4.2.5 c47a5959
4.2.5.1 Resolution: 1.5
4.2.5.1.1 DoubletFinder Doublet Assignment
4.2.5.1.2 DoubletFinder Doublet Score
4.2.5.1.3 Density of Doublet Score
4.2.5.1.4 Violin of Doublet Score
4.2.5.1.5 Parameters
| useAssay | counts |
| seed | 12345 |
| seuratNfeatures | 2000 |
| seuratPcs | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
| seuratRes | 1.5 |
| formationRate | 0.075 |
| nCores | NULL |
| verbose | FALSE |
| packageVersion | 2.0.2 |
runDoubletFinder relies on a parameter (in Seurat)
called resolution to determine cells that may be doublets. Users will be
able to manipulate the resolution parameter through
seuratRes. If multiple numeric vectors are stored in
seuratRes, there will be multiple label/scores. The
seuratNfeatures parameter determines the number of features
that is used in the FindVariableFeatures function in
Seurat. seuratPcs parameter determines the number of
dimensions used in the FindNeighbors function in Seurat.
The formationRate parameter is the estimated doublet
detection rate in the dataset. aims to detect doublets by creating
simulated doublets from combining transcriptomic profiles of existing
cells in the dataset.
4.3 ScDblFinder
scDblFinder
is a doublet detection algorithm in the scran package.
scDblFinder aims to detect doublets by creating a simulated doublet from
existing cells and projecting it to the same PCA space as the cells. The
wrapper function runScDblFinder can be used to separately
run the scDblFinder algorithm on its own. The wrapper function
plotScDblFinderResults can be used to plot the QC outputs
from the scDblFinder algorithm. The output of scDblFinder is a
scDblFinder_doublet_score and
scDblFinder_doublet_call. The doublet score of a droplet
will be higher if the it is deemed likely to be a doublet.
4.3.1 d8b737fb
4.3.1.1 ScDblFinder Doublet Assignment
4.3.1.2 ScDblFinder Doublet Score
4.3.1.3 Density Score
4.3.1.4 Violin Score
4.3.1.5 Parameters
| useAssay | counts |
| nNeighbors | 50 |
| simDoublets | 19985 |
| seed | 12345 |
| packageVersion | 1.8.0 |
The nNeighbors parameter is the number of nearest
neighbor used to calculate the density for doublet detection.
simDoublets is used to determine the number of simulated
doublets used for doublet detection.
4.3.2 8628f96c
4.3.2.1 ScDblFinder Doublet Assignment
4.3.2.2 ScDblFinder Doublet Score
4.3.2.3 Density Score
4.3.2.4 Violin Score
4.3.2.5 Parameters
| useAssay | counts |
| nNeighbors | 50 |
| simDoublets | 19985 |
| seed | 12345 |
| packageVersion | 1.8.0 |
The nNeighbors parameter is the number of nearest
neighbor used to calculate the density for doublet detection.
simDoublets is used to determine the number of simulated
doublets used for doublet detection.
4.3.3 e7372715
4.3.3.1 ScDblFinder Doublet Assignment
4.3.3.2 ScDblFinder Doublet Score
4.3.3.3 Density Score
4.3.3.4 Violin Score
4.3.3.5 Parameters
| useAssay | counts |
| nNeighbors | 50 |
| simDoublets | 19985 |
| seed | 12345 |
| packageVersion | 1.8.0 |
The nNeighbors parameter is the number of nearest
neighbor used to calculate the density for doublet detection.
simDoublets is used to determine the number of simulated
doublets used for doublet detection.
4.3.4 84824920
4.3.4.1 ScDblFinder Doublet Assignment
4.3.4.2 ScDblFinder Doublet Score
4.3.4.3 Density Score
4.3.4.4 Violin Score
4.3.4.5 Parameters
| useAssay | counts |
| nNeighbors | 50 |
| simDoublets | 19985 |
| seed | 12345 |
| packageVersion | 1.8.0 |
The nNeighbors parameter is the number of nearest
neighbor used to calculate the density for doublet detection.
simDoublets is used to determine the number of simulated
doublets used for doublet detection.
4.3.5 c47a5959
4.3.5.1 ScDblFinder Doublet Assignment
4.3.5.2 ScDblFinder Doublet Score
4.3.5.3 Density Score
4.3.5.4 Violin Score
4.3.5.5 Parameters
| useAssay | counts |
| nNeighbors | 50 |
| simDoublets | 19985 |
| seed | 12345 |
| packageVersion | 1.8.0 |
The nNeighbors parameter is the number of nearest
neighbor used to calculate the density for doublet detection.
simDoublets is used to determine the number of simulated
doublets used for doublet detection.
4.4 Cxds
CXDS, or co-expression based doublet scoring, is an algorithm in the
SCDS
package which employs a binomial model for the co-expression of pairs of
genes to determine doublets. The wrapper function runCxds
can be used to separately run the CXDS algorithm on its own. The wrapper
function plotCxdsResults can be used to plot the QC outputs
from the CXDS algorithm. The output of runCxds is the doublet score,
scds_cxds_score.
4.4.1 d8b737fb
4.4.1.1 Cxds Doublet Assignment
4.4.1.2 Cxds Doublet Score
4.4.1.3 Density Score
4.4.1.4 Violin Score
4.4.1.5 Parameters
| seed | 12345 |
| ntop | 500 |
| binThresh | 0 |
| verb | FALSE |
| retRes | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.10.0 |
In runCxds, the ntop parameter is the number of top
variance genes to consider. The binThresh parameter is the
minimum counts a gene needs to have to be included in the analysis.
verb determines whether progress messages will be displayed
or not. retRes will determine whether the gene pair results
should be returned or not. The user may set the estimated number of
doublets with estNdbl.
4.4.2 8628f96c
4.4.2.1 Cxds Doublet Assignment
4.4.2.2 Cxds Doublet Score
4.4.2.3 Density Score
4.4.2.4 Violin Score
4.4.2.5 Parameters
| seed | 12345 |
| ntop | 500 |
| binThresh | 0 |
| verb | FALSE |
| retRes | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.10.0 |
In runCxds, the ntop parameter is the number of top
variance genes to consider. The binThresh parameter is the
minimum counts a gene needs to have to be included in the analysis.
verb determines whether progress messages will be displayed
or not. retRes will determine whether the gene pair results
should be returned or not. The user may set the estimated number of
doublets with estNdbl.
4.4.3 e7372715
4.4.3.1 Cxds Doublet Assignment
4.4.3.2 Cxds Doublet Score
4.4.3.3 Density Score
4.4.3.4 Violin Score
4.4.3.5 Parameters
| seed | 12345 |
| ntop | 500 |
| binThresh | 0 |
| verb | FALSE |
| retRes | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.10.0 |
In runCxds, the ntop parameter is the number of top
variance genes to consider. The binThresh parameter is the
minimum counts a gene needs to have to be included in the analysis.
verb determines whether progress messages will be displayed
or not. retRes will determine whether the gene pair results
should be returned or not. The user may set the estimated number of
doublets with estNdbl.
4.4.4 84824920
4.4.4.1 Cxds Doublet Assignment
4.4.4.2 Cxds Doublet Score
4.4.4.3 Density Score
4.4.4.4 Violin Score
4.4.4.5 Parameters
| seed | 12345 |
| ntop | 500 |
| binThresh | 0 |
| verb | FALSE |
| retRes | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.10.0 |
In runCxds, the ntop parameter is the number of top
variance genes to consider. The binThresh parameter is the
minimum counts a gene needs to have to be included in the analysis.
verb determines whether progress messages will be displayed
or not. retRes will determine whether the gene pair results
should be returned or not. The user may set the estimated number of
doublets with estNdbl.
4.4.5 c47a5959
4.4.5.1 Cxds Doublet Assignment
4.4.5.2 Cxds Doublet Score
4.4.5.3 Density Score
4.4.5.4 Violin Score
4.4.5.5 Parameters
| seed | 12345 |
| ntop | 500 |
| binThresh | 0 |
| verb | FALSE |
| retRes | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.10.0 |
In runCxds, the ntop parameter is the number of top
variance genes to consider. The binThresh parameter is the
minimum counts a gene needs to have to be included in the analysis.
verb determines whether progress messages will be displayed
or not. retRes will determine whether the gene pair results
should be returned or not. The user may set the estimated number of
doublets with estNdbl.
4.5 Bcds
BCDS, or binary classification based doublet scoring, is an algorithm
in the SCDS
package which uses a binary classification approach to determine
doublets. The wrapper function runBcds can be used to
separately run the BCDS algorithm on its own. The wrapper function
plotBCDSResults can be used to plot the QC outputs from the
BCDS algorithm. The output of runBcds is scds_bcds_score,
which is the likelihood that a cell is a doublet.
4.5.1 d8b737fb
4.5.1.1 Bcds Doublet Assignment
4.5.1.2 Bcds Doublet Score
4.5.1.3 Density Score
4.5.1.4 Violin Score
4.5.1.5 Parameters
| seed | 12345 |
| ntop | 500 |
| srat | 1 |
| verb | FALSE |
| retRes | FALSE |
| nmax | tune |
| varImp | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.10.0 |
In runBcds, the ntop parameter is the number of top
variance genes to consider. The srat parameter is the ratio
between original number of cells and simulated doublets. The
nmax parameter is the maximum number of cycles that the
algorithm should run through. If set to tune, this will be
automatic. The varImp parameter determines if the variable
importance should be returned or not.
4.5.2 8628f96c
4.5.2.1 Bcds Doublet Assignment
4.5.2.2 Bcds Doublet Score
4.5.2.3 Density Score
4.5.2.4 Violin Score
4.5.2.5 Parameters
| seed | 12345 |
| ntop | 500 |
| srat | 1 |
| verb | FALSE |
| retRes | FALSE |
| nmax | tune |
| varImp | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.10.0 |
In runBcds, the ntop parameter is the number of top
variance genes to consider. The srat parameter is the ratio
between original number of cells and simulated doublets. The
nmax parameter is the maximum number of cycles that the
algorithm should run through. If set to tune, this will be
automatic. The varImp parameter determines if the variable
importance should be returned or not.
4.5.3 e7372715
4.5.3.1 Bcds Doublet Assignment
4.5.3.2 Bcds Doublet Score
4.5.3.3 Density Score
4.5.3.4 Violin Score
4.5.3.5 Parameters
| seed | 12345 |
| ntop | 500 |
| srat | 1 |
| verb | FALSE |
| retRes | FALSE |
| nmax | tune |
| varImp | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.10.0 |
In runBcds, the ntop parameter is the number of top
variance genes to consider. The srat parameter is the ratio
between original number of cells and simulated doublets. The
nmax parameter is the maximum number of cycles that the
algorithm should run through. If set to tune, this will be
automatic. The varImp parameter determines if the variable
importance should be returned or not.
4.5.4 84824920
4.5.4.1 Bcds Doublet Assignment
4.5.4.2 Bcds Doublet Score
4.5.4.3 Density Score
4.5.4.4 Violin Score
4.5.4.5 Parameters
| seed | 12345 |
| ntop | 500 |
| srat | 1 |
| verb | FALSE |
| retRes | FALSE |
| nmax | tune |
| varImp | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.10.0 |
In runBcds, the ntop parameter is the number of top
variance genes to consider. The srat parameter is the ratio
between original number of cells and simulated doublets. The
nmax parameter is the maximum number of cycles that the
algorithm should run through. If set to tune, this will be
automatic. The varImp parameter determines if the variable
importance should be returned or not.
4.5.5 c47a5959
4.5.5.1 Bcds Doublet Assignment
4.5.5.2 Bcds Doublet Score
4.5.5.3 Density Score
4.5.5.4 Violin Score
4.5.5.5 Parameters
| seed | 12345 |
| ntop | 500 |
| srat | 1 |
| verb | FALSE |
| retRes | FALSE |
| nmax | tune |
| varImp | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.10.0 |
In runBcds, the ntop parameter is the number of top
variance genes to consider. The srat parameter is the ratio
between original number of cells and simulated doublets. The
nmax parameter is the maximum number of cycles that the
algorithm should run through. If set to tune, this will be
automatic. The varImp parameter determines if the variable
importance should be returned or not.
4.6 ScdsHybrid
The CXDS-BCDS hybrid algorithm, uses both CXDS and BCDS algorithms
from the SCDS
package. The wrapper function runCxdsBcdsHybrid can be used
to separately run the CXDS-BCDS hybrid algorithm on its own. The wrapper
function plotScdsHybridResults can be used to plot the QC
outputs from the CXDS-BCDS hybrid algorithm. The output of
runCxdsBcdsHybrid is the doublet score,
scds_hybrid_score.
4.6.1 d8b737fb
4.6.1.1 ScdsHybrid Doublet Assignment
4.6.1.2 ScdsHybrid Doublet Score
4.6.1.3 Density Score
4.6.1.4 Violin Score
4.6.1.5 Parameters
| seed | 12345 |
| nTop | 500 |
| cxdsArgs | NULL |
| bcdsArgs | NULL |
| verb | FALSE |
| estNdbl | TRUE |
| force | FALSE |
| useAssay | counts |
| packageVersion | 1.10.0 |
All parameters from the runBCDS and runBCDS
functions may be applied to this function in the cxdsArgs
and bcdsArgs parameters, respectively.
4.6.2 8628f96c
4.6.2.1 ScdsHybrid Doublet Assignment
4.6.2.2 ScdsHybrid Doublet Score
4.6.2.3 Density Score
4.6.2.4 Violin Score
4.6.2.5 Parameters
| seed | 12345 |
| nTop | 500 |
| cxdsArgs | NULL |
| bcdsArgs | NULL |
| verb | FALSE |
| estNdbl | TRUE |
| force | FALSE |
| useAssay | counts |
| packageVersion | 1.10.0 |
All parameters from the runBCDS and runBCDS
functions may be applied to this function in the cxdsArgs
and bcdsArgs parameters, respectively.
4.6.3 e7372715
4.6.3.1 ScdsHybrid Doublet Assignment
4.6.3.2 ScdsHybrid Doublet Score
4.6.3.3 Density Score
4.6.3.4 Violin Score
4.6.3.5 Parameters
| seed | 12345 |
| nTop | 500 |
| cxdsArgs | NULL |
| bcdsArgs | NULL |
| verb | FALSE |
| estNdbl | TRUE |
| force | FALSE |
| useAssay | counts |
| packageVersion | 1.10.0 |
All parameters from the runBCDS and runBCDS
functions may be applied to this function in the cxdsArgs
and bcdsArgs parameters, respectively.
4.6.4 84824920
4.6.4.1 ScdsHybrid Doublet Assignment
4.6.4.2 ScdsHybrid Doublet Score
4.6.4.3 Density Score
4.6.4.4 Violin Score
4.6.4.5 Parameters
| seed | 12345 |
| nTop | 500 |
| cxdsArgs | NULL |
| bcdsArgs | NULL |
| verb | FALSE |
| estNdbl | TRUE |
| force | FALSE |
| useAssay | counts |
| packageVersion | 1.10.0 |
All parameters from the runBCDS and runBCDS
functions may be applied to this function in the cxdsArgs
and bcdsArgs parameters, respectively.
4.6.5 c47a5959
4.6.5.1 ScdsHybrid Doublet Assignment
4.6.5.2 ScdsHybrid Doublet Score
4.6.5.3 Density Score
4.6.5.4 Violin Score
4.6.5.5 Parameters
| seed | 12345 |
| nTop | 500 |
| cxdsArgs | NULL |
| bcdsArgs | NULL |
| verb | FALSE |
| estNdbl | TRUE |
| force | FALSE |
| useAssay | counts |
| packageVersion | 1.10.0 |
All parameters from the runBCDS and runBCDS
functions may be applied to this function in the cxdsArgs
and bcdsArgs parameters, respectively.
5 Ambient RNA Detection
5.1 Ambient RNA Detection Summary
5.1.1 decontX
5.2 DecontX
In droplet-based single cell technologies, ambient RNA that may have
been released from apoptotic or damaged cells may get incorporated into
another droplet, and can lead to contamination. decontX,
available from the celda,
is a Bayesian method for the identification of the contamination level
at a cellular level. The wrapper function runDecontX can be
used to separately run the DecontX algorithm on its own. The wrapper
function plotDecontXResults can be used to plot the QC
outputs from the DecontX algorithm. The outputs of
runDecontX are decontX_contamination and
decontX_clusters. decontX_contamination is a
numeric vector which characterizes the level of contamination in each
cell. Clustering is performed as part of the runDecontX
algorithm. decontX_clusters is the resulting cluster
assignment, which can also be labeled on the plot.
5.2.1 d8b737fb
5.2.1.1 DecontX Contamination Score
5.2.1.2 DecontX Clusters
5.2.1.3 Density Score
5.2.1.4 Violin Score
5.2.1.5 Parameters
| z | NULL |
| maxIter | 500 |
| delta | 10 10 |
| estimateDelta | TRUE |
| convergence | 0.001 |
| varGenes | 5000 |
| dbscanEps | 1 |
| logfile | NULL |
| verbose | TRUE |
| packageVersion | 1.12.0 |
5.2.2 8628f96c
5.2.2.1 DecontX Contamination Score
5.2.2.2 DecontX Clusters
5.2.2.3 Density Score
5.2.2.4 Violin Score
5.2.2.5 Parameters
| z | NULL |
| maxIter | 500 |
| delta | 10 10 |
| estimateDelta | TRUE |
| convergence | 0.001 |
| varGenes | 5000 |
| dbscanEps | 1 |
| logfile | NULL |
| verbose | TRUE |
| packageVersion | 1.12.0 |
5.2.3 e7372715
5.2.3.1 DecontX Contamination Score
5.2.3.2 DecontX Clusters
5.2.3.3 Density Score
5.2.3.4 Violin Score
5.2.3.5 Parameters
| z | NULL |
| maxIter | 500 |
| delta | 10 10 |
| estimateDelta | TRUE |
| convergence | 0.001 |
| varGenes | 5000 |
| dbscanEps | 1 |
| logfile | NULL |
| verbose | TRUE |
| packageVersion | 1.12.0 |
5.2.4 84824920
5.2.4.1 DecontX Contamination Score
5.2.4.2 DecontX Clusters
5.2.4.3 Density Score
5.2.4.4 Violin Score
5.2.4.5 Parameters
| z | NULL |
| maxIter | 500 |
| delta | 10 10 |
| estimateDelta | TRUE |
| convergence | 0.001 |
| varGenes | 5000 |
| dbscanEps | 1 |
| logfile | NULL |
| verbose | TRUE |
| packageVersion | 1.12.0 |
5.2.5 c47a5959
5.2.5.1 DecontX Contamination Score
5.2.5.2 DecontX Clusters
5.2.5.3 Density Score
5.2.5.4 Violin Score
5.2.5.5 Parameters
| z | NULL |
| maxIter | 500 |
| delta | 10 10 |
| estimateDelta | TRUE |
| convergence | 0.001 |
| varGenes | 5000 |
| dbscanEps | 1 |
| logfile | NULL |
| verbose | TRUE |
| packageVersion | 1.12.0 |
5.3 SoupX
In droplet-based single cell technologies, ambient RNA that may have
been released from apoptotic or damaged cells may get incorporated into
another droplet, and can lead to contamination. SoupX uses
non-expressed genes to estimates a global contamination fraction.The
wrapper function runSoupX can be used to separately run the
SoupX algorithm on its own. The main outputs of runSoupX
are soupX_contamination, soupX_clusters, and
the corrected assay SoupX, together with other intermediate
metrics that SoupX generates.soupX_contamination is a
numeric vector which characterizes the level of contamination in each
cell. SoupX generates one global contamination estimate per sample,
instead of returning cell-specific estimation.Clustering is required for
SoupX algorithm. It will be performed if users do not provide the label
as input. quickCluster() method from package scran is
adopted for this purpose. soupX_clusters is the resulting
cluster assignment, which can also be labeled on the plot. The wrapper
function plotSoupXResult can be used to plot the QC outputs
from the SoupX algorithm. Plots includes a UMAP with clustering labels
and a number of UMAPs colored with the soup fraction of top marker genes
which are identified for contamination estimation.
5.3.1 d8b737fb
5.3.1.1 SoupX Clustering
5.3.1.2 Soup Fractions
5.3.1.2.1 ENSG00000127329
5.3.1.2.2 ENSG00000186335
5.3.1.2.3 ENSG00000169347
5.3.1.2.4 ENSG00000242029
5.3.1.2.5 ENSG00000251504
5.3.1.3 Parameters
| useAssay | counts |
| bgAssayName | NULL |
| assayName | SoupX |
| tfidfMin | 1 |
| soupQuantile | 0.9 |
| maxMarkers | 100 |
| contaminationRange | 0.01 0.8 |
| rhoMaxFDR | 0.2 |
| priorRho | 0.05 |
| priorRhoStdDev | 0.1 |
| forceAccept | FALSE |
| adjustMethod | subtraction |
| roundToInt | FALSE |
| tol | 0.001 |
| pCut | 0.01 |
| reducedDimName | SoupX_UMAP_d8b737fb |
| sessionInfo | x86_64-pc-linux-gnu |
| cluster | soupX_clusters |
5.3.2 8628f96c
5.3.2.1 SoupX Clustering
5.3.2.2 Soup Fractions
5.3.2.2.1 ENSG00000070915
5.3.2.2.2 ENSG00000074803
5.3.2.2.3 ENSG00000109684
5.3.2.2.4 ENSG00000183580
5.3.2.2.5 ENSG00000251504
5.3.2.3 Parameters
| useAssay | counts |
| bgAssayName | NULL |
| assayName | SoupX |
| tfidfMin | 1 |
| soupQuantile | 0.9 |
| maxMarkers | 100 |
| contaminationRange | 0.01 0.8 |
| rhoMaxFDR | 0.2 |
| priorRho | 0.05 |
| priorRhoStdDev | 0.1 |
| forceAccept | FALSE |
| adjustMethod | subtraction |
| roundToInt | FALSE |
| tol | 0.001 |
| pCut | 0.01 |
| reducedDimName | SoupX_UMAP_8628f96c |
| sessionInfo | x86_64-pc-linux-gnu |
| cluster | soupX_clusters |
5.3.3 e7372715
5.3.3.1 SoupX Clustering
5.3.3.2 Soup Fractions
5.3.3.2.1 ENSG00000143882
5.3.3.2.2 ENSG00000169344
5.3.3.2.3 ENSG00000127329
5.3.3.2.4 ENSG00000186335
5.3.3.2.5 ENSG00000070915
5.3.3.3 Parameters
| useAssay | counts |
| bgAssayName | NULL |
| assayName | SoupX |
| tfidfMin | 1 |
| soupQuantile | 0.9 |
| maxMarkers | 100 |
| contaminationRange | 0.01 0.8 |
| rhoMaxFDR | 0.2 |
| priorRho | 0.05 |
| priorRhoStdDev | 0.1 |
| forceAccept | FALSE |
| adjustMethod | subtraction |
| roundToInt | FALSE |
| tol | 0.001 |
| pCut | 0.01 |
| reducedDimName | SoupX_UMAP_e7372715 |
| sessionInfo | x86_64-pc-linux-gnu |
| cluster | soupX_clusters |
5.3.4 84824920
5.3.4.1 SoupX Clustering
5.3.4.2 Soup Fractions
5.3.4.2.1 ENSG00000070915
5.3.4.2.2 ENSG00000127329
5.3.4.2.3 ENSG00000148942
5.3.4.2.4 ENSG00000183287
5.3.4.2.5 ENSG00000119121
5.3.4.3 Parameters
| useAssay | counts |
| bgAssayName | NULL |
| assayName | SoupX |
| tfidfMin | 1 |
| soupQuantile | 0.9 |
| maxMarkers | 100 |
| contaminationRange | 0.01 0.8 |
| rhoMaxFDR | 0.2 |
| priorRho | 0.05 |
| priorRhoStdDev | 0.1 |
| forceAccept | FALSE |
| adjustMethod | subtraction |
| roundToInt | FALSE |
| tol | 0.001 |
| pCut | 0.01 |
| reducedDimName | SoupX_UMAP_84824920 |
| sessionInfo | x86_64-pc-linux-gnu |
| cluster | soupX_clusters |
5.3.5 c47a5959
5.3.5.1 SoupX Clustering
5.3.5.2 Soup Fractions
5.3.5.2.1 ENSG00000242029
5.3.5.2.2 ENSG00000074803
5.3.5.2.3 ENSG00000250799
5.3.5.2.4 ENSG00000251504
5.3.5.2.5 ENSG00000143882
5.3.5.3 Parameters
| useAssay | counts |
| bgAssayName | NULL |
| assayName | SoupX |
| tfidfMin | 1 |
| soupQuantile | 0.9 |
| maxMarkers | 100 |
| contaminationRange | 0.01 0.8 |
| rhoMaxFDR | 0.2 |
| priorRho | 0.05 |
| priorRhoStdDev | 0.1 |
| forceAccept | FALSE |
| adjustMethod | subtraction |
| roundToInt | FALSE |
| tol | 0.001 |
| pCut | 0.01 |
| reducedDimName | SoupX_UMAP_c47a5959 |
| sessionInfo | x86_64-pc-linux-gnu |
| cluster | soupX_clusters |
6 Session Information
Session Information
## R version 4.1.1 (2021-08-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
##
## Matrix products: default
## BLAS: /share/pkg.7/r/4.1.1_noblas/install/lib64/R/lib/libRblas.so
## LAPACK: /share/pkg.7/r/4.1.1_noblas/install/lib64/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8
## [4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] TENxPBMCData_1.12.0 HDF5Array_1.22.1 rhdf5_2.38.1
## [4] cowplot_1.1.1 dplyr_1.0.10 ggplot2_3.3.6
## [7] singleCellTK_2.7.1 DelayedArray_0.20.0 Matrix_1.5-0
## [10] SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0 Biobase_2.54.0
## [13] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1 IRanges_2.28.0
## [16] S4Vectors_0.32.4 BiocGenerics_0.40.0 MatrixGenerics_1.6.0
## [19] matrixStats_0.62.0
##
## loaded via a namespace (and not attached):
## [1] AnnotationHub_3.2.2 BiocFileCache_2.2.1 systemfonts_1.0.4
## [4] BiocParallel_1.28.3 scater_1.22.0 digest_0.6.29
## [7] htmltools_0.5.3 viridis_0.6.2 fansi_1.0.3
## [10] magrittr_2.0.3 memoise_2.0.1 ScaledMatrix_1.2.0
## [13] GSVAdata_1.30.0 limma_3.50.3 Biostrings_2.62.0
## [16] R.utils_2.12.0 svglite_2.1.0 colorspace_2.0-3
## [19] blob_1.2.3 rvest_1.0.2 rappdirs_0.3.3
## [22] ggrepel_0.9.1 xfun_0.32 crayon_1.5.1
## [25] RCurl_1.98-1.8 jsonlite_1.8.0 glue_1.6.2
## [28] kableExtra_1.3.4 gtable_0.3.1 zlibbioc_1.40.0
## [31] XVector_0.34.0 webshot_0.5.2 BiocSingular_1.10.0
## [34] DropletUtils_1.14.2 Rhdf5lib_1.16.0 fishpond_2.0.1
## [37] scales_1.2.1 DBI_1.1.3 edgeR_3.36.0
## [40] Rcpp_1.0.9 viridisLite_0.4.1 xtable_1.8-6
## [43] reticulate_1.26 dqrng_0.3.0 bit_4.0.4
## [46] rsvd_1.0.5 httr_1.4.4 FNN_1.1.3.1
## [49] ellipsis_0.3.2 pkgconfig_2.0.3 R.methodsS3_1.8.2
## [52] farver_2.1.1 scuttle_1.4.0 sass_0.4.2
## [55] uwot_0.1.14 dbplyr_2.2.1 locfit_1.5-9.6
## [58] utf8_1.2.2 tidyselect_1.1.2 labeling_0.4.2
## [61] rlang_1.0.5 later_1.3.0 AnnotationDbi_1.56.2
## [64] munsell_0.5.0 BiocVersion_3.14.0 tools_4.1.1
## [67] cachem_1.0.6 cli_3.4.0 generics_0.1.3
## [70] RSQLite_2.2.17 ExperimentHub_2.2.1 evaluate_0.16
## [73] stringr_1.4.1 fastmap_1.1.0 yaml_2.3.5
## [76] knitr_1.40 bit64_4.0.5 purrr_0.3.4
## [79] KEGGREST_1.34.0 sparseMatrixStats_1.6.0 mime_0.12
## [82] R.oo_1.25.0 xml2_1.3.3 compiler_4.1.1
## [85] rstudioapi_0.14 beeswarm_0.4.0 filelock_1.0.2
## [88] curl_4.3.2 png_0.1-7 interactiveDisplayBase_1.32.0
## [91] tibble_3.1.8 bslib_0.4.0 stringi_1.7.8
## [94] highr_0.9 lattice_0.20-45 vctrs_0.4.1
## [97] pillar_1.8.1 lifecycle_1.0.2 rhdf5filters_1.6.0
## [100] BiocManager_1.30.18 jquerylib_0.1.4 RcppAnnoy_0.0.19
## [103] BiocNeighbors_1.12.0 bitops_1.0-7 irlba_2.3.5
## [106] httpuv_1.6.6 R6_2.5.1 promises_1.2.0.1
## [109] gridExtra_2.3 vipor_0.4.5 codetools_0.2-18
## [112] gtools_3.9.3 assertthat_0.2.1 withr_2.5.0
## [115] GenomeInfoDbData_1.2.7 parallel_4.1.1 grid_4.1.1
## [118] beachmat_2.10.0 rmarkdown_2.16 DelayedMatrixStats_1.16.0
## [121] shiny_1.7.2 ggbeeswarm_0.6.0